Retrieval coverage limits LLM rerankers in cold-start recommendation; a learned hybrid fusion improves pool quality but LLM reranking often degrades end-to-end performance while simpler rankers exploit the pool.
Learning to warm up cold item embeddings for cold-start recommendation with 14 meta scaling and shifting networks
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
The anti-lexicographic SUS-anchor achieves sampling densities less than 1% above the lower bound for alphabet size 4 and k=1, substantially outperforming bidirectional anchors.
UNICS pre-trains on a pseudocode dataset for cross-lingual logic then applies multi-task transfer learning with hard-positive mining and dynamic hard-negative sampling to reach claimed SOTA on multilingual code-search benchmarks.
citing papers explorer
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Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation
Retrieval coverage limits LLM rerankers in cold-start recommendation; a learned hybrid fusion improves pool quality but LLM reranking often degrades end-to-end performance while simpler rankers exploit the pool.
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The anti-lexicographic SUS-anchor: a near-optimal k=1 sampling scheme
The anti-lexicographic SUS-anchor achieves sampling densities less than 1% above the lower bound for alphabet size 4 and k=1, substantially outperforming bidirectional anchors.
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UNICS: Multilingual Code Search via Unified Pseudocode and Contrastive Transfer Learning
UNICS pre-trains on a pseudocode dataset for cross-lingual logic then applies multi-task transfer learning with hard-positive mining and dynamic hard-negative sampling to reach claimed SOTA on multilingual code-search benchmarks.